import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    Optional[int]:
		if model.config.model_type == "gpt2":
				return model.transformer.h[0].mlp.c_fc
		return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
				import torch
				import torch.nn as nn





				class     lowerCAmelCase__     (				nn.Module			):


						'''simple docstring'''






						def __init__(       self     :     Optional[int]     ,  snake_case__     :     nn.Module     ,  snake_case__     :     int		)      ->	int:
								super().__init__()
								_lowerCamelCase				   =  module
								_lowerCamelCase				   =  nn.Sequential(
								    nn.Linear(module.in_features     ,  snake_case__     ,  bias=snake_case__		)     ,  nn.Linear(snake_case__     ,  module.out_features     ,  bias=snake_case__		)     ,  )
								_lowerCamelCase				   =  (2.0 / (5 * min(module.in_features     ,  module.out_features		))) ** 0.5
								nn.init.normal_(self.adapter[0].weight     ,  std=snake_case__		)
								nn.init.zeros_(self.adapter[1].weight		)
								self.adapter.to(module.weight.device		)



						def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     List[Any]     ,  *snake_case__     :     List[str]     ,  **snake_case__     :     List[str]		)      ->	Dict:
								return self.module(snake_case__     ,  *snake_case__     ,  **snake_case__		) + self.adapter(snake_case__		)





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   'bigscience/bloom-1b7'

		# Constant values
		lowerCAmelCase_     =   2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

		lowerCAmelCase_     =   'Hello my name is'
		lowerCAmelCase_     =   set()
		EXPECTED_OUTPUTS.add('Hello my name is John and I am a professional photographer. I'			)
		EXPECTED_OUTPUTS.add('Hello my name is John.\nI am a friend of your father.\n'			)
		EXPECTED_OUTPUTS.add('Hello my name is John Doe, I am a student at the University'			)
		lowerCAmelCase_     =   10
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Optional[int]:
				# Models and tokenizer
				_lowerCamelCase				   =  AutoTokenizer.from_pretrained(self.model_name		)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Dict		)      ->	Tuple:
				super().setUp()

				# Models and tokenizer
				_lowerCamelCase				   =  AutoModelForCausalLM.from_pretrained(
				    self.model_name     ,  torch_dtype=torch.floataa     ,  device_map='auto'		)
				_lowerCamelCase				   =  AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=snake_case__     ,  device_map='auto'		)
		def   _snake_case       (       self     :     Dict		)      ->	List[str]:
				del self.model_fpaa
				del self.model_abit

				gc.collect()
				torch.cuda.empty_cache()
		def   _snake_case       (       self     :     Any		)      ->	int:
				_lowerCamelCase				   =  self.model_abit.config

				self.assertTrue(hasattr(snake_case__     ,  'quantization_config'		)		)

				_lowerCamelCase				   =  config.to_dict()
				_lowerCamelCase				   =  config.to_diff_dict()

				_lowerCamelCase				   =  config.to_json_string()
		def   _snake_case       (       self     :     List[str]		)      ->	str:
				from bitsandbytes.nn import Paramsabit

				_lowerCamelCase				   =  self.model_fpaa.get_memory_footprint()
				_lowerCamelCase				   =  self.model_abit.get_memory_footprint()

				self.assertAlmostEqual(mem_fpaa / mem_abit     ,  self.EXPECTED_RELATIVE_DIFFERENCE		)
				_lowerCamelCase				   =  get_some_linear_layer(self.model_abit		)
				self.assertTrue(linear.weight.__class__ == Paramsabit		)
		def   _snake_case       (       self     :     Dict		)      ->	Any:
				from transformers import TaPreTrainedModel

				self.model_fpaa.get_memory_footprint()
				self.model_abit.get_memory_footprint()

				for name, module in self.model_abit.named_modules():
						if isinstance(snake_case__     ,  torch.nn.Linear		):
								if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
										# 4-bit parameters are packed in uint8 variables
										self.assertTrue(module.weight.dtype == torch.uinta		)
		def   _snake_case       (       self     :     Dict		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  self.tokenizer(self.input_text     ,  return_tensors='pt'		)
				_lowerCamelCase				   =  self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0		)     ,  max_new_tokens=1_0		)

				self.assertIn(self.tokenizer.decode(output_sequences[0]     ,  skip_special_tokens=snake_case__		)     ,  self.EXPECTED_OUTPUTS		)
		def   _snake_case       (       self     :     Any		)      ->	List[Any]:
				_lowerCamelCase				   =  BitsAndBytesConfig()
				_lowerCamelCase				   =  True

				_lowerCamelCase				   =  AutoModelForCausalLM.from_pretrained(
				    self.model_name     ,  quantization_config=snake_case__     ,  device_map='auto'		)

				_lowerCamelCase				   =  self.tokenizer(self.input_text     ,  return_tensors='pt'		)
				_lowerCamelCase				   =  model_abit_from_config.generate(
				    input_ids=encoded_input['input_ids'].to(0		)     ,  max_new_tokens=1_0		)

				self.assertIn(self.tokenizer.decode(output_sequences[0]     ,  skip_special_tokens=snake_case__		)     ,  self.EXPECTED_OUTPUTS		)
		def   _snake_case       (       self     :     Tuple		)      ->	int:
				with self.assertRaises(snake_case__		), tempfile.TemporaryDirectory() as tmpdirname:
						self.model_abit.save_pretrained(snake_case__		)
		def   _snake_case       (       self     :     int		)      ->	Optional[Any]:
				_lowerCamelCase				   =  BitsAndBytesConfig()

				with self.assertRaises(snake_case__		):
						_lowerCamelCase				   =  AutoModelForCausalLM.from_pretrained(
						    self.model_name     ,  quantization_config=snake_case__     ,  load_in_abit=snake_case__     ,  device_map='auto'     ,  bnb_abit_quant_type='nf4'     ,  )
		def   _snake_case       (       self     :     Any		)      ->	Tuple:
				with self.assertRaises(snake_case__		):
						# Tries with `str`
						self.model_abit.to('cpu'		)

				with self.assertRaises(snake_case__		):
						# Tries with a `dtype``
						self.model_abit.to(torch.floataa		)

				with self.assertRaises(snake_case__		):
						# Tries with a `device`
						self.model_abit.to(torch.device('cuda:0'		)		)

				with self.assertRaises(snake_case__		):
						# Tries with a `device`
						self.model_abit.float()

				with self.assertRaises(snake_case__		):
						# Tries with a `device`
						self.model_abit.half()

				# Test if we did not break anything
				_lowerCamelCase				   =  self.tokenizer(self.input_text     ,  return_tensors='pt'		)

				_lowerCamelCase				   =  self.model_fpaa.to(torch.floataa		)
				_lowerCamelCase				   =  self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0		)     ,  max_new_tokens=1_0		)

				# Check this does not throw an error
				_lowerCamelCase				   =  self.model_fpaa.to('cpu'		)

				# Check this does not throw an error
				_lowerCamelCase				   =  self.model_fpaa.half()

				# Check this does not throw an error
				_lowerCamelCase				   =  self.model_fpaa.float()



		def   _snake_case       (       self     :     Dict		)      ->	int:
				_lowerCamelCase				   =  AutoModelForSeqaSeqLM.from_pretrained('t5-small'     ,  load_in_abit=snake_case__     ,  device_map='auto'		)
				self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa		)





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		@classmethod
		def   _snake_case       (       cls     :     int		)      ->	Optional[int]:
				_lowerCamelCase				   =  't5-small'
				_lowerCamelCase				   =  'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
				_lowerCamelCase				   =  AutoTokenizer.from_pretrained(cls.model_name		)
				_lowerCamelCase				   =  'Translate in German: Hello, my dog is cute'
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	int:
				gc.collect()
				torch.cuda.empty_cache()
		def   _snake_case       (       self     :     Tuple		)      ->	List[Any]:
				from transformers import TaForConditionalGeneration

				_lowerCamelCase				   =  TaForConditionalGeneration._keep_in_fpaa_modules
				_lowerCamelCase				   =  None

				# test with `t5-small`
				_lowerCamelCase				   =  TaForConditionalGeneration.from_pretrained(self.model_name     ,  load_in_abit=snake_case__     ,  device_map='auto'		)
				_lowerCamelCase				   =  self.tokenizer(self.input_text     ,  return_tensors='pt'		).to(0		)
				_lowerCamelCase				   =  model.generate(**snake_case__		)

				# test with `flan-t5-small`
				_lowerCamelCase				   =  TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name     ,  load_in_abit=snake_case__     ,  device_map='auto'		)
				_lowerCamelCase				   =  self.tokenizer(self.input_text     ,  return_tensors='pt'		).to(0		)
				_lowerCamelCase				   =  model.generate(**snake_case__		)
				_lowerCamelCase				   =  modules



		def   _snake_case       (       self     :     List[str]		)      ->	Dict:
				import bitsandbytes as bnb

				from transformers import TaForConditionalGeneration

				# test with `t5-small`
				_lowerCamelCase				   =  TaForConditionalGeneration.from_pretrained(self.model_name     ,  load_in_abit=snake_case__     ,  device_map='auto'		)

				# there was a bug with decoders - this test checks that it is fixed
				self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q     ,  bnb.nn.Linearabit		)		)

				_lowerCamelCase				   =  self.tokenizer(self.input_text     ,  return_tensors='pt'		).to(0		)
				_lowerCamelCase				   =  model.generate(**snake_case__		)

				# test with `flan-t5-small`
				_lowerCamelCase				   =  TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name     ,  load_in_abit=snake_case__     ,  device_map='auto'		)
				_lowerCamelCase				   =  self.tokenizer(self.input_text     ,  return_tensors='pt'		).to(0		)
				_lowerCamelCase				   =  model.generate(**snake_case__		)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Any		)      ->	Optional[int]:
				super().setUp()
				# model_name
				_lowerCamelCase				   =  'bigscience/bloom-560m'
				_lowerCamelCase				   =  't5-small'

				# Different types of model

				_lowerCamelCase				   =  AutoModel.from_pretrained(self.model_name     ,  load_in_abit=snake_case__     ,  device_map='auto'		)
				# Sequence classification model
				_lowerCamelCase				   =  AutoModelForSequenceClassification.from_pretrained(
				    self.model_name     ,  load_in_abit=snake_case__     ,  device_map='auto'		)
				# CausalLM model
				_lowerCamelCase				   =  AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=snake_case__     ,  device_map='auto'		)
				# Seq2seq model
				_lowerCamelCase				   =  AutoModelForSeqaSeqLM.from_pretrained(
				    self.seq_to_seq_name     ,  load_in_abit=snake_case__     ,  device_map='auto'		)
		def   _snake_case       (       self     :     Optional[int]		)      ->	Union[str, Any]:
				del self.base_model
				del self.sequence_model
				del self.model_abit
				del self.seq_to_seq_model

				gc.collect()
				torch.cuda.empty_cache()



		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				from bitsandbytes.nn import Paramsabit

				self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit		)

				# Other heads should be nn.Parameter
				self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter		)
				self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter		)
				self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter		)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def   _snake_case       (       self     :     List[Any]		)      ->	List[str]:
				super().setUp()
		def   _snake_case       (       self     :     str		)      ->	str:
				del self.pipe

				gc.collect()
				torch.cuda.empty_cache()



		def   _snake_case       (       self     :     List[Any]		)      ->	Any:
				_lowerCamelCase				   =  pipeline(
				    'text-generation'     ,  model=self.model_name     ,  model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa}     ,  max_new_tokens=self.MAX_NEW_TOKENS     ,  )

				# Real second forward pass
				_lowerCamelCase				   =  self.pipe(self.input_text		)
				self.assertIn(pipeline_output[0]['generated_text']     ,  self.EXPECTED_OUTPUTS		)





@require_torch_multi_gpu
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Optional[int]		)      ->	Union[str, Any]:
				super().setUp()



		def   _snake_case       (       self     :     Optional[int]		)      ->	Tuple:
				_lowerCamelCase				   =  AutoModelForCausalLM.from_pretrained(
				    self.model_name     ,  load_in_abit=snake_case__     ,  device_map='balanced'		)

				# Check correct device map
				self.assertEqual(set(model_parallel.hf_device_map.values()		)     ,  {0, 1}		)

				# Check that inference pass works on the model
				_lowerCamelCase				   =  self.tokenizer(self.input_text     ,  return_tensors='pt'		)

				# Second real batch
				_lowerCamelCase				   =  model_parallel.generate(input_ids=encoded_input['input_ids'].to(0		)     ,  max_new_tokens=1_0		)
				self.assertIn(self.tokenizer.decode(output_parallel[0]     ,  skip_special_tokens=snake_case__		)     ,  self.EXPECTED_OUTPUTS		)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Optional[int]		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  'facebook/opt-350m'
				super().setUp()



		def   _snake_case       (       self     :     List[Any]		)      ->	Tuple:
				if version.parse(importlib.metadata.version('bitsandbytes'		)		) < version.parse('0.37.0'		):
						return

				# Step 1: freeze all parameters
				_lowerCamelCase				   =  AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=snake_case__		)

				self.assertEqual(set(model.hf_device_map.values()		)     ,  {torch.cuda.current_device()}		)

				for param in model.parameters():
						_lowerCamelCase				   =  False  # freeze the model - train adapters later
						if param.ndim == 1:
								# cast the small parameters (e.g. layernorm) to fp32 for stability
								_lowerCamelCase				   =  param.data.to(torch.floataa		)

        # Step 2: add adapters
				for _, module in model.named_modules():
						if "OPTAttention" in repr(type(snake_case__		)		):
								_lowerCamelCase				   =  LoRALayer(module.q_proj     ,  rank=1_6		)
								_lowerCamelCase				   =  LoRALayer(module.k_proj     ,  rank=1_6		)
								_lowerCamelCase				   =  LoRALayer(module.v_proj     ,  rank=1_6		)

        # Step 3: dummy batch
				_lowerCamelCase				   =  self.tokenizer('Test batch '     ,  return_tensors='pt'		).to(0		)

				# Step 4: Check if the gradient is not None
				with torch.cuda.amp.autocast():
						_lowerCamelCase				   =  model.forward(**snake_case__		)
						out.logits.norm().backward()

				for module in model.modules():
						if isinstance(snake_case__     ,  snake_case__		):
								self.assertTrue(module.adapter[1].weight.grad is not None		)
								self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0		)
						elif isinstance(snake_case__     ,  nn.Embedding		):
								self.assertTrue(module.weight.grad is None		)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   'gpt2-xl'
		lowerCAmelCase_     =   3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7
from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge





@total_ordering
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __lt__(       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				return self[-1] < other[-1]



		def __eq__(       self     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	Union[str, Any]:
				return self[-1] == other[-1]



def 	lowerCamelCase					(	UpperCamelCase     :				list							)   ->    list:
		_lowerCamelCase				   =  []
		# sort into stacks
		for element in collection:
				_lowerCamelCase				   =  Stack([element]							)
				_lowerCamelCase				   =  bisect_left(UpperCamelCase			,       UpperCamelCase							)
				if i != len(UpperCamelCase							):
						stacks[i].append(UpperCamelCase							)
				else:
						stacks.append(UpperCamelCase							)

    # use a heap-based merge to merge stack efficiently
		_lowerCamelCase				   =  merge(*(reversed(UpperCamelCase							) for stack in stacks)							)
		return collection


if __name__ == "__main__":
				A						=			input('Enter numbers separated by a comma:\n').strip()
				A						=			[int(item) for item in user_input.split(',')]
				print(patience_sort(unsorted))